Ace the SRM Exam 2026 – Elevate Your Stats Skills and Conquer the Risk!

Bagging, or Bootstrap Aggregating, is a powerful ensemble learning technique that combines multiple models to improve prediction accuracy and reduce variance. The correct statement reflects that bagging is specifically designed to address the issue of overfitting, which is particularly pronounced in complex models.

By training multiple models on different subsets of the data, each obtained through bootstrapping (random sampling with replacement), bagging creates an ensemble that smoothens out the predictions. This allows the model to generalize better to unseen data. Although individual models may overfit to their respective training sets, the aggregation of their predictions typically leads to a more robust and stable performance as the model is less sensitive to the noise in any single training set.

In contrast to the notion of increasing variance, bagging typically serves to reduce overall model variance, which is critical in improving model generalization. It is not limited to decision trees; it can be applied to a variety of model types. Furthermore, bagging is well-known for improving prediction accuracy rather than diminishing it, as the ensemble approach leverages the strengths of multiple learners.